Summary: Area C: Genome-wide identification and targeting of resistance to cancer therapy The frequent emergence of resistance to anti-cancer therapies remains a major challenge in cancer treatment that is of utmost importance. Recent clinical and experimental studies addressing this problem require the arduous collection of pre- and post- treatment data for every new specific treatment and cancer type studied. Thus, a computational approach that can expedite the identification of molecular determinants of resistance via the analysis of existing large-scale cancer cohorts is called for. Our proposal seeks to identify novel ways to counter de novo and acquired resistance through drug combinations. We focus on gene interactions rather than individual genes and leverage large scale genomic datasets and patient response data. Our approach is based on recent work in the Ruppin and other labs showing that genetic interactions can be computationally identified by analyzing omics tumor data. To decipher pathways of resistance to cancer therapies, we focus here on studying a new type of genetic interactions, termed synthetic rescues (SRs). SRs denote a functional interaction between two genes whereby a fitness reducing change in the activity of one of the two genes (termed the vulnerable gene) is compensated by altered activity of another gene (termed the rescuer gene), which restores cell fitness and rescues it. We have recently developed tools for SR data-driven identification from large cancer tumors cohorts, successfully enabling the prediction of drug response and emergence of resistance in patients. Building on these transformational results the specific aims of this proposal are: Specific Aim 1. Perform a pan-cancer and cancer type-specific SR-based analyses, focusing on melanoma, breast, head and neck, and colon cancer, identifying the major rescuer genes in each cancer type, together with specific recommendations of combinatorial therapies mitigating resistance. Specific Aim 2. Develop a new version of SR analysis tools that will be made publically available for use by others in a standard, user friendly manner and includes analysis of gene methylation and genome wide mutation data, on top of other omics data already utilized in our previous SR inference tools. Specific Aim 3. Experimentally test predicted rescuer targets and combination therapies in patient derived resistant cancer cells. Taken together the proposed study, will present a transformative SR based approach for identifying and targeting resistance pathways across the whole cancer genome.